Artificial intelligence is increasingly diffusing into design and creative education, reshaping how students ideate, iterate, and produce work. While these tools offer clear benefits in terms of speed and experimentation, they also challenge long-standing pedagogical assumptions regarding the importance of process, authorship, and studio-based learning. Educators are therefore faced with a dual task: mitigating the risks AI poses to reflective, process-oriented design while simultaneously exploring ways to meaningfully integrate these technologies into curricula. Through working group activities, this workshop examines these tensions to identify emerging strategies for the critical and transparent use of AI. Ultimately, we aim to address current knowledge gaps, establish a collaborative research agenda, and build a network to support AI collaboration in design and creative education.

We report on design-focused inquiry into future multiple sclerosis (MS) healthcare; including a multi-stage design process with experienced MS clinicians, and formative evaluations with people living with MS. MS is a chronic, progressive, and unpredictable inflammatory neurological disease of the central nervous system that affects at least 2.8 million people worldwide. Walking impairments affect up to 85% of people diagnosed with MS. Responding to this, our focus is on design for longitudinally monitoring mobility, and in particular using wearable sensors that generate data on gait metrics to support clinical and self-care decision-making. We contribute to HCI research in three ways: (1) a detailed case study design process, including artifacts; (2) metaphorical framing concepts, with associated use cases illustrated through design scenarios; and (3) understanding of virtual-first practices in rehabilitation medicine that can be translated beyond MS care.

This paper reports on a research through design (RtD) inquiry into public perceptions of transparency of Internet of Things (IoT) sensors increasingly deployed within urban neighborhoods as part of smart city programs. In particular, we report on the results of three participatory design workshops during which 40 New York City residents used physical signage as a medium for materializing transparency concerns about several sensors. We found that people’s concerns went beyond making sensors more transparent but instead sought to reveal the technology’s interconnected social, political, and economic processes. Building from these findings, we highlight the opportunities to move from treating transparency as an object to treating it as an ongoing activity. We argue that this move opens opportunities for designers and policy-makers to provide meaningful and actionable transparency of smart cities.

Open data programs have become increasingly established at national and local levels of government. While the degree of success these programs have had in achieving their objectives remains open to question, one factor that has been identified as important to any success is the role of open data intermediaries, individuals and organizations that help others to make use of open data. In this paper we investigate how people become engaged with open data, what their motivations are, and the barriers and facilitators program participants perceive with regard to using open data effectively. We interview participants from a variety of backgrounds with differing levels of experience and engagement with open data. Participants include students learning how to train others in open data techniques and tools; people who attend open data events and use open data for commercial or social benefit; and representatives from local government, municipal agencies and a civic tech non-profit. We identify pathways to successfully developing and nurturing a community of open data intermediaries, and make five recommendations for organizations planning and managing open data programs.

Walking impairment is a debilitating symptom of Multiple Sclerosis (MS), a disease affecting 2.8 million people worldwide. While clinicians’ in-person observational gait assessments are important, research suggests that data from wearable sensors can indicate early onset of gait impairment, track patients’ responses to treatment, and support remote and longitudinal assessment. We present an inquiry into supporting the transition from research to clinical practice. Co-design by HCI, biomedical, neurology and rehabilitation researchers resulted in a data-rich interface prototype for augmented gait analysis based on visualized sensor data. We used this as a prompt in interviews with ten experienced clinicians from a range of MS rehabilitation roles. We find that clinicians value quantitative sensor data within a whole patient narrative, to help track specific rehabilitation goals, but identify a tension between grasping critical information quickly and more detailed understanding. Based on the findings we make design recommendations for data-rich remote rehabilitation interfaces.

Abstract Noise is one of the primary quality‐of‐life issues in urban environments. In addition to annoyance, noise negatively impacts public health and educational performance. While low‐cost sensors can be deployed to monitor ambient noise levels at high temporal resolutions, the amount of data they produce and the complexity of these data pose significant analytical challenges. One way to address these challenges is through machine listening techniques, which are used to extract features in attempts to classify the source of noise and understand temporal patterns of a city's noise situation. However, the overwhelming number of noise sources in the urban environment and the scarcity of labeled data makes it nearly impossible to create classification models with large enough vocabularies that capture the true dynamism of urban soundscapes. In this paper, we first identify a set of requirements in the yet unexplored domain of urban soundscape exploration. To satisfy the requirements and tackle the identified challenges, we propose Urban Rhapsody, a framework that combines state‐of‐the‐art audio representation, machine learning and visual analytics to allow users to interactively create classification models, understand noise patterns of a city, and quickly retrieve and label audio excerpts in order to create a large high‐precision annotated database of urban sound recordings. We demonstrate the tool's utility through case studies performed by domain experts using data generated over the five‐year deployment of a one‐of‐a‐kind sensor network in New York City.

Noise pollution is among the most consistently cited and highest impact quality-of-life issues in major urban areas across the US, with more than 70 million people estimated to be exposed to noise levels considered harmful. While HCI and CSCW has a relatively rich history of engagement with monitoring such environmental concerns, e.g. through participatory sensing, prior research has not to our knowledge engaged with the process of municipal mitigation. In this paper we present research in this direction, connecting support for pervasive environmental monitoring with civic engagement in mitigation action. Having first identified and described the research space for this engagement, we present empirical data from an ongoing case study focused on two communities living with chronic problem noise. We probe the experiences of residents and representatives of different municipal organizations tasked with addressing their concerns. We find that making and drawing on records of noise is important to residents and authorities, that these groups have misaligned perceptions of how effective current reporting programs are, and that communication between them can be poor. We also find that noise is often one part of more complex issues. We then discuss our findings in light of prior research, and identify a model of civic sensing that highlights opportunities for HCI design and research including: supporting residents' coordinated actions and actions with municipal open data, mediating residents' and authorities' assessments of data quality, and supporting accountability and attributable mitigation action.

This paper investigates the tools and practices used by Orientation and Mobility (O&M) specialists in instructing people who are blind or have low vision in concepts, skills, and techniques for safe and independent travel. Based on interviews with experienced instructors who practice in different O&M settings we find that a shortage of qualified specialists and restrictions on in-person activities during COVID-19 has accelerated interest in remote instruction and assessment, while widespread adoption of smartphones with accessibility support has driven interest in assistive apps. This presents both opportunities and challenges for a practice that is traditionally conducted in-person and assessed through qualitative observations. In response we identify multiple opportunities for HCI research in service of O&M, including: supporting a 'physician's assistant' model of remote O&M instruction and assessment, matching O&M instructors' clients with guide dogs, highlighting clients' progress towards O&M goals, and collaboratively planning routes and monitoring clients' independent travel progress.

Urban planning is increasingly data driven, yet the challenge of designing with data at a city scale and remaining sensitive to the impact at a human scale is as important today as it was for Jane Jacobs. We address this challenge with Urban Mosaic, a tool for exploring the urban fabric through a spatially and temporally dense data set of 7.7 million street-level images from New York City, captured over the period of a year. Working in collaboration with professional practitioners, we use Urban Mosaic to investigate questions of accessibility and mobility, and preservation and retrofitting. In doing so, we demonstrate how tools such as this might provide a bridge between the city and the street, by supporting activities such as visual comparison of geographically distant neighborhoods, and temporal analysis of unfolding urban development.

Machine learning (ML) poses complex challenges for user experience (UX) designers. Typically unpredictable and opaque, it may produce unforeseen outcomes detrimental to particular groups or individuals, yet simultaneously promise amazing breakthroughs in areas as diverse as medical diagnosis and universal translation. This results in a polarized view of ML, which is often manifested through a technology-as-monster metaphor. In this paper, we acknowledge the power and potential of this metaphor by resurfacing historic complexities in human-monster relations. We (re)introduce these liminal and ambiguous creatures, and discuss their relation to ML. We offer a background to designers' use of metaphor, and show how the technology-as-monster metaphor can generatively probe and (re)frame the questions ML poses. We illustrate the effectiveness of this approach through a detailed discussion of an early-stage generative design workshop inquiring into ML approaches to supporting student mental health and well-being.

Applications in a range of domains, including route planning and well-being, offer advice based on the social information available in prior users' aggregated activity. When designing these applications, is it better to offer: a) advice that if strictly adhered to is more likely to result in an individual successfully achieving their goal, even if fewer users will choose to adopt it? or b) advice that is likely to be adopted by a larger number of users, but which is sub-optimal with regard to any particular individual achieving their goal? We identify this dilemma, characterized as Goal-Directed vs. Adoption-Directed advice, and investigate the design questions it raises through an online experiment undertaken in four advice domains (financial investment, making healthier lifestyle choices, route planning, training for a 5k run), with three user types, and across two levels of uncertainty. We report findings that suggest a preference for advice favoring individual goal attainment over higher user adoption rates, albeit with significant variation across advice domains; and discuss their design implications.

Annotating rich audio data is an essential aspect of training and evaluating machine listening systems. We approach this task in the context of temporally-complex urban soundscapes, which require multiple labels to identify overlapping sound sources. Typically this work is crowdsourced, and previous studies have shown that workers can quickly label audio with binary annotation for single classes. However, this approach can be difficult to scale when multiple passes with different focus classes are required to annotate data with multiple labels. In citizen science, where tasks are often image-based, annotation efforts typically label multiple classes simultaneously in a single pass. This paper describes our data collection on the Zooniverse citizen science platform, comparing the efficiencies of different audio annotation strategies. We compared multiple-pass binary annotation, single-pass multi-label annotation, and a hybrid approach: hierarchical multi-pass multi-label annotation. We discuss our findings, which support using multi-label annotation, with reference to volunteer citizen scientists' motivations.

Machine learning (ML) is now a fairly established technology, and user experience (UX) designers appear regularly to integrate ML services in new apps, devices, and systems. Interestingly, this technology has not experienced a wealth of design innovation that other technologies have, and this might be because it is a new and difficult design material. To better understand why we have witnessed little design innovation, we conducted a survey of current UX practitioners with regards to how new ML services are envisioned and developed in UX practice. Our survey probed on how ML may or may not have been a part of their UX design education, on how they work to create new things with developers, and on the challenges they have faced working with this material. We use the findings from this survey and our review of related literature to present a series of challenges for UX and interaction design research and education. Finally, we discuss areas where new research and new curriculum might help our community unlock the power of design thinking to re-imagine what ML might be and might do.

Exploring interactive visualizations of data generated within the domain for which new products and services are to be designed can play a useful role in stimulating ideas that are considered highly appropriate to that domain. We describe a study in which participants in four collaborative design workshops used information visualizations representing electricity consumption data to help generate ideas for new products and services that could utilise the data generated by a smart home. Participants in the workshops appeared to use sensemaking behaviour to develop insights about the domain, which were later used in generating new ideas. Ideas arising from workshops where the stimulus was data visualized with less ambiguity in the visual encoding were judged to be significantly more appropriate than those from workshops where ambiguity in the visual encoding of the data used as stimulus was intentionally increased. We discuss the implications of this with regards to designing future workshop activities.

Creativity workshops have proved effective in drawing out unexpected requirements and giving form to participants' novel ideas. Here, we introduce a new addition to the workshop designer's toolkit: interactive data visualization, used as stimuli to prompt insight and inspire creativity. We first describe a pilot study in which we compare the effectiveness of two different styles of data visualization. Here we found that a less ambiguous style was more effective in supporting idea generation. Following this, we report a case study in which we employ data visualization within a service design workshop, where participants gain insights that are later realized in design ideas.

We enhance a user-centered design process with techniques that deliberately promote creativity to identify opportunities for the visualization of data generated by a major energy supplier. Visualization prototypes developed in this way prove effective in a situation whereby data sets are largely unknown and requirements open - enabling successful exploration of possibilities for visualization in Smart Home data analysis. The process gives rise to novel designs and design metaphors including data sculpting. It suggests: that the deliberate use of creativity techniques with data stakeholders is likely to contribute to successful, novel and effective solutions; that being explicit about creativity may contribute to designers developing creative solutions; that using creativity techniques early in the design process may result in a creative approach persisting throughout the process. The work constitutes the first systematic visualization design for a data rich source that will be increasingly important to energy suppliers and consumers as Smart Meter technology is widely deployed. It is novel in explicitly employing creativity techniques at the requirements stage of visualization design and development, paving the way for further use and study of creativity methods in visualization design.